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Found 12 papers, 5 papers with code
Representing Joint Hierarchies with Box Embeddings
Box Embeddings [Vilnis et al., 2018, Li et al., 2019] represent concepts with hyperrectangles in $n$-dimensional space and are shown to be capable of modeling tree-like structures efficiently by training on a large subset of the transitive closure of the WordNet hypernym graph.
Looking Beyond Sentence-Level Natural Language Inference for Downstream Tasks
In recent years, the Natural Language Inference (NLI) task has garnered significant attention, with new datasets and models achieving near human-level performance on it.
Reading Comprehension as Natural Language Inference: A Semantic Analysis
We transform the one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms.
Weakly Supervised Medication Regimen Extraction from Medical Conversations
Automated Medication Regimen (MR) extraction from medical conversations can not only improve recall and help patients follow through with their care plan, but also reduce the documentation burden for doctors.
Reading Comprehension as Natural Language Inference:A Semantic Analysis
We transform one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms.
Looking Beyond Sentence-Level Natural Language Inference for Question Answering and Text Summarization
Natural Language Inference (NLI) has garnered significant attention in recent years; however, the promise of applying NLI breakthroughs to other downstream NLP tasks has remained unfulfilled.
Word2Box: Capturing Set-Theoretic Semantics of Words using Box Embeddings
In this work, we provide a fuzzy-set interpretation of box embeddings, and learn box representations of words using a set-theoretic training objective.
Box Embeddings: An open-source library for representation learning using geometric structures
A major factor contributing to the success of modern representation learning is the ease of performing various vector operations.
Structured Energy Network as a dynamic loss function. Case study. A case study with multi-label Classification
The best version of SEAL that uses NCE ranking method achieves close to +2. 85, +2. 23 respective F1 point gain in average over cross-entropy and INFNET on the feature-based datasets, excluding one outlier that has an excessive gain of +50. 0 F1 points.
Pretrained Language Models as Visual Planners for Human Assistance
Given a succinct natural language goal, e. g., "make a shelf", and a video of the user's progress so far, the aim of VPA is to devise a plan, i. e., a sequence of actions such as "sand shelf", "paint shelf", etc.
Language Guided Exploration for RL Agents in Text Environments
Real-world sequential decision making is characterized by sparse rewards and large decision spaces, posing significant difficulty for experiential learning systems like $\textit{tabula rasa}$ reinforcement learning (RL) agents.
Event-Event Relation Extraction using Probabilistic Box Embedding
To understand a story with multiple events, it is important to capture the proper relations across these events.
